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Attribute Selection for a Discrete Choice Experiment Incorporating a Best-Worst Scaling Survey.

Edward J D Webb1, David Meads1, Yvonne Lynch2

  • 1Leeds Institute of Health Sciences, University of Leeds, Leeds, England, UK; Choice Modelling Centre, University of Leeds, Leeds, England, UK.

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|April 12, 2021
PubMed
Summary

This study used best-worst scaling (BWS-1) to select attributes for a discrete choice experiment (DCE) on augmentative and alternative communication (AAC) systems. BWS-1 successfully guided attribute selection, informing future research in this area.

Keywords:
attribute developmentattribute selectionbest-worst scalingdiscrete choice experimentmethodology

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Area of Science:

  • Health Services Research
  • Decision Science
  • Rehabilitation Engineering

Background:

  • Qualitative methods are used to generate attributes for discrete choice experiments (DCEs).
  • Limited guidance exists on selecting which attributes to include in DCEs.
  • This study addresses attribute selection for augmentative and alternative communication (AAC) systems.

Purpose of the Study:

  • To present a case study using best-worst scaling case 1 (BWS-1) to guide attribute selection for a DCE.
  • To inform the decision-making process of professionals choosing AAC systems for children with limited natural speech.

Main Methods:

  • Attributes for BWS-1 were derived from literature reviews and focus groups.
  • DCE attributes were selected based on BWS-1 relative importance scores, coherence, research aims, and respondent burden.
  • The BWS-1 survey included 19 child and 18 AAC system attributes (N=93 professionals); the DCE included 4 child and 5 AAC system attributes (N=155 professionals).

Main Results:

  • BWS-1 relative importance scores were used to judge attribute importance.
  • Four child and five AAC device/system attributes were selected for the DCE.
  • The BWS-1 survey successfully guided the selection of attributes for the DCE.

Conclusions:

  • Best-worst scaling (BWS-1) is a valuable tool for discrete choice experiment (DCE) attribute selection, especially when prior stated preference data is scarce.
  • Recommendations for future studies include defining selection criteria a priori, considering participant perspectives, and clearly defining terminology.
  • BWS-1 is particularly useful in areas with limited existing research or when qualitative work is challenging.